IDENTIFICATION OF DYNAMICALLY POSITIONED SHIPS

Todays model-based dynamic positioning (DP) systems require that the s hip and thruster dynamics are known with some accuracy in order to use linear quadratic optical control theory. However, it is difficult to identify the mathematical model of a dynamically positioned (DP) ship since the ship is not persistently excited under DP. In addition the s hip parameter estimation problem is nonlinear and multivariable with o nly position and thruster state measurements available for parameter e stimation. The process and measurement noise must also be modeled in o rder to avoid parameter drift due to environmental disturbances and se nsor failure. This article discusses an off-line parallel extended Kal man filter (EKF) algorithm utilizing two measurement series in paralle l to estimate the parameters in the DP ship model. Full-scale experime nts with a supply vessel are used to demonstrate the convergence and r obustness of the proposed parameter estimator.